DSAI: Unbiased and Interpretable Latent Feature Extraction for Data-Centric AI

TL;DR

DSAI is a novel framework that extracts unbiased, interpretable latent features from large datasets, addressing limitations of LLMs in data grounding and enabling better data-driven insights.

Contribution

The paper introduces DSAI, a multi-stage pipeline with quantifiable metrics for unbiased, interpretable feature extraction directly from data.

Findings

High recall in synthetic datasets with known features

Effective uncovering of meaningful patterns in real-world data

Supports interpretable classification with minimal expert input

Abstract

Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets due to their reliance on pre-trained knowledge rather than actual data patterns. To address this data grounding issue, we propose Data Scientist AI (DSAI), a framework that enables unbiased and interpretable feature extraction through a multi-stage pipeline with quantifiable prominence metrics for evaluating extracted features. On synthetic datasets with known ground-truth features, DSAI demonstrates high recall in identifying expert-defined features while faithfully reflecting the underlying data. Applications on real-world datasets illustrate the framework's practical utility in uncovering meaningful patterns with minimal expert oversight, supporting use cases such as interpretable classification. The title of our paper is chosen from multiple candidates based on DSAI-generated criteria.